7 citations · 9 across the 5 of their papers we have counts for
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cs.LG2024★ 2 cited
Chaos-based reinforcement learning with TD3
Toshitaka Matsuki, Yusuke Sakemi, Kazuyuki Aihara
Chaos-based reinforcement learning (CBRL) is a method in which the agent's internal chaotic dynamics drives exploration. However, the learning algorithms in CBRL have not been thor…
cs.LG2023
Learning Reservoir Dynamics with Temporal Self-Modulation
Yusuke Sakemi, Sou Nobukawa, Toshitaka Matsuki +2
Reservoir computing (RC) can efficiently process time-series data by transferring the input signal to randomly connected recurrent neural networks (RNNs), which are referred to as…
cs.LG2022
Deep Q-network using reservoir computing with multi-layered readout
Toshitaka Matsuki
Recurrent neural network (RNN) based reinforcement learning (RL) is used for learning context-dependent tasks and has also attracted attention as a method with remarkable learning…